roadoi interacts with the oaDOI API, a simple web-interface which links DOIs and open access versions of scholarly works. oaDOI powers unpaywall.
This client supports the most recent API Version 2.
API Documentation: http://oadoi.org/api/v2
Use the oadoi_fetch() function in this package to get open access status
information and full-text links from oaDOI.
roadoi::oadoi_fetch(dois = c("10.1038/ng.3260", "10.1093/nar/gkr1047"),
email = "[email protected]")
#> # A tibble: 2 x 13
#> doi best_oa_location oa_locations data_standard
#> <chr> <list> <list> <int>
#> 1 10.1038/ng.3260 <tibble [1 x 6]> <tibble [1 x 7]> 2
#> 2 10.1093/nar/gkr1047 <tibble [1 x 7]> <tibble [4 x 7]> 2
#> # ... with 9 more variables: is_oa <lgl>, journal_is_oa <lgl>,
#> # journal_issns <chr>, journal_name <chr>, publisher <chr>, title <chr>,
#> # year <chr>, updated <chr>, non_compliant <list>There are no API restrictions. However, providing an email address is required and a rate limit of 100k is suggested. If you need to access more data, ask for the data dump https://oadoi.org/api instead.
This package also has a RStudio Addin for easily finding free full-texts in RStudio.
Install and load from CRAN:
install.packages("roadoi")
library(roadoi)To install the development version, use the devtools package
devtools::install_github("ropensci/roadoi")
library(roadoi)Open access copies of scholarly publications are sometimes hard to find. Some are published in open access journals. Others are made freely available as preprints before publication, and others are deposited in institutional repositories, digital archives maintained by universities and research institutions. This document guides you to roadoi, a R client that makes it easy to search for these open access copies by interfacing the oaDOI.org service where DOIs are matched with freely available full-texts available from open access journals and archives.
oaDOI.org, developed and maintained by the team of Impactstory, is a non-profit service that finds open access copies of scholarly literature simply by looking up a DOI (Digital Object Identifier). It not only returns open access full-text links, but also helpful metadata about the open access status of a publication such as licensing or provenance information.
oaDOI.org uses different data sources to find open access full-texts including:
- Crossref: a DOI registration agency serving major scholarly publishers.
- Datacite: another DOI registration agency with main focus on research data
- Directory of Open Access Journals (DOAJ): a registry of open access journals
- Bielefeld Academic Search Engine (BASE): an aggregator of various OAI-PMH metadata sources. OAI-PMH is a protocol often used by open access journals and repositories.
See Piwowar et al. (2017) for a comprehensive overview of oaDOI.org.1
There is one major function to talk with oaDOI.org, oadoi_fetch(), taking a character vector of DOIs and your email address as required arguments.
library(roadoi)
roadoi::oadoi_fetch(dois = c("10.1186/s12864-016-2566-9",
"10.1016/j.cognition.2014.07.007"),
email = "[email protected]")
#> # A tibble: 2 x 13
#> doi best_oa_location oa_locations
#> <chr> <list> <list>
#> 1 10.1186/s12864-016-2566-9 <tibble [1 x 7]> <tibble [3 x 7]>
#> 2 10.1016/j.cognition.2014.07.007 <tibble [1 x 6]> <tibble [2 x 7]>
#> # ... with 10 more variables: data_standard <int>, is_oa <lgl>,
#> # journal_is_oa <lgl>, journal_issns <chr>, journal_name <chr>,
#> # publisher <chr>, title <chr>, year <chr>, updated <chr>,
#> # non_compliant <list>The client supports API version 2. According to the oaDOI.org API specification, the following variables with the following definitions are returned:
| Column | Description |
|---|---|
doi |
DOI (always in lowercase) |
best_oa_location |
list-column describing the best OA location. Algorithm prioritizes publisher hosted content (e.g. Hybrid or Gold) |
oa_locations |
list-column of all the OA locations. |
data_standard |
Indicates the data collection approaches used for this resource. 1 mostly uses Crossref for hybrid detection. 2 uses more comprehensive hybrid detection methods. |
is_oa |
Is there an OA copy (logical)? |
journal_is_oa |
Is the article published in a fully OA journal? Uses the Directory of Open Access Journals (DOAJ) as source. |
journal_issns |
ISSNs |
journal_name |
Journal title |
publisher |
Publisher |
title |
Publication title. |
year |
Year published. |
updated |
Time when the data for this resource was last updated. |
non_compliant |
Lists other full-text resources that are not hosted by either publishers or repositories. |
The columns best_oa_location and oa_locations are list-columns
that contain useful metadata about the OA sources found by oaDOI. These are
| Column | Description |
|---|---|
evidence |
How the OA location was found and is characterized by oaDOI? |
host_type |
OA full-text provided by publisher or repository. |
license |
The license under which this copy is published |
url |
The URL where you can find this OA copy. |
versions |
The content version accessible at this location following the DRIVER 2.0 Guidelines (https://wiki.surfnet.nl/display/DRIVERguidelines/DRIVER-VERSION+Mappings) |
You can simplify these list-columns in at least two ways.
To get the full-text links from the list-column best_oa_location, you may want to use purrr::map_chr().
library(dplyr)
roadoi::oadoi_fetch(dois = c("10.1186/s12864-016-2566-9",
"10.1016/j.cognition.2014.07.007"),
email = "[email protected]") %>%
dplyr::mutate(urls = purrr::map_chr(best_oa_location, "url")) %>%
.$urls
#> [1] "https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-016-2566-9?site=bmcgenomics.biomedcentral.com"
#> [2] "http://pubman.mpdl.mpg.de/pubman/item/escidoc:2070098/component/escidoc:2070097/Guerra_knoeferle_2014.pdf"If you want to gather all full-text links and to explore where these links are hosted, simplify the list-column oa_locations with tidyr::unnest():
library(dplyr)
roadoi::oadoi_fetch(dois = c("10.1186/s12864-016-2566-9",
"10.1016/j.cognition.2014.07.007"),
email = "[email protected]") %>%
tidyr::unnest(oa_locations) %>%
dplyr::mutate(
hostname = purrr::map(url, httr::parse_url) %>%
purrr::map_chr(., "hostname", .null = NA_integer_)
) %>%
dplyr::mutate(hostname = gsub("www.", "", hostname)) %>%
dplyr::count(hostname)
#> # A tibble: 4 x 2
#> hostname n
#> <chr> <int>
#> 1 bmcgenomics.biomedcentral.com 1
#> 2 ncbi.nlm.nih.gov 1
#> 3 pub.uni-bielefeld.de 2
#> 4 pubman.mpdl.mpg.de 1Note that fields to be returned might change according to the oaDOI.org API specs
There are no API restrictions. However, providing your email address when using this client is required by oaDOI.org. Set email address in your .Rprofile file with the option roadoi_email when you are too tired to type in your email address every time you want to call oaDOI.org.
options(roadoi_email = "[email protected]")To follow your API call, and to estimate the time until completion, use the .progress parameter inherited from plyr to display a progress bar.
roadoi::oadoi_fetch(dois = c("10.1186/s12864-016-2566-9",
"10.1016/j.cognition.2014.07.007"),
email = "[email protected]",
.progress = "text")
#>
|
| | 0%
|
|================================ | 50%
|
|=================================================================| 100%
#> # A tibble: 2 x 13
#> doi best_oa_location oa_locations
#> <chr> <list> <list>
#> 1 10.1186/s12864-016-2566-9 <tibble [1 x 7]> <tibble [3 x 7]>
#> 2 10.1016/j.cognition.2014.07.007 <tibble [1 x 6]> <tibble [2 x 7]>
#> # ... with 10 more variables: data_standard <int>, is_oa <lgl>,
#> # journal_is_oa <lgl>, journal_issns <chr>, journal_name <chr>,
#> # publisher <chr>, title <chr>, year <chr>, updated <chr>,
#> # non_compliant <list>oaDOI is a reliable API. However, this client follows Hadley Wickham's Best practices for writing an API package and throws an error when the API does not return valid JSON or is not available. To catch these errors, you may want to use plyr's failwith() function
random_dois <- c("ldld", "10.1038/ng.3260", "§dldl ")
purrr::map_df(random_dois,
plyr::failwith(f = function(x) roadoi::oadoi_fetch(x, email ="[email protected]")))
#> # A tibble: 1 x 13
#> doi best_oa_location oa_locations data_standard is_oa
#> <chr> <list> <list> <int> <lgl>
#> 1 10.1038/ng.3260 <tibble [1 x 6]> <tibble [1 x 7]> 2 TRUE
#> # ... with 8 more variables: journal_is_oa <lgl>, journal_issns <chr>,
#> # journal_name <chr>, publisher <chr>, title <chr>, year <chr>,
#> # updated <chr>, non_compliant <list>An increasing number of universities, research organisations and funders have launched open access policies in recent years. Using roadoi together with other R-packages makes it easy to examine how and to what extent researchers comply with these policies in a reproducible and transparent manner. In particular, the rcrossref package, maintained by rOpenSci, provides many helpful functions for this task.
DOIs have become essential for referencing scholarly publications, and thus many digital libraries and institutional databases keep track of these persistent identifiers. For the sake of this vignette, instead of starting with a pre-defined set of publications originating from these sources, we simply generate a random sample of 100 DOIs registered with Crossref by using the rcrossref package.
library(dplyr)
library(rcrossref)
# get a random sample of DOIs and metadata describing these works
random_dois <- rcrossref::cr_r(sample = 100) %>%
rcrossref::cr_works() %>%
.$data
random_dois
#> # A tibble: 100 x 35
#> alternative.id
#> <chr>
#> 1
#> 2
#> 3
#> 4 10.1080/10739149908085828
#> 5
#> 6
#> 7
#> 8 S0266462300004918
#> 9
#> 10 BF00202270
#> # ... with 90 more rows, and 34 more variables: container.title <chr>,
#> # created <chr>, deposited <chr>, DOI <chr>, funder <list>,
#> # indexed <chr>, ISBN <chr>, ISSN <chr>, issue <chr>, issued <chr>,
#> # link <list>, member <chr>, page <chr>, prefix <chr>, publisher <chr>,
#> # reference.count <chr>, score <chr>, source <chr>, subject <chr>,
#> # title <chr>, type <chr>, URL <chr>, assertion <list>, author <list>,
#> # `clinical-trial-number` <list>, volume <chr>, archive <chr>,
#> # license_date <chr>, license_URL <chr>, license_delay.in.days <chr>,
#> # license_content.version <chr>, subtitle <chr>, abstract <chr>,
#> # update.policy <chr>Let's see when these random publications were published
random_dois %>%
# convert to years
mutate(issued, issued = lubridate::parse_date_time(issued, c('y', 'ymd', 'ym'))) %>%
mutate(issued, issued = lubridate::year(issued)) %>%
group_by(issued) %>%
summarize(pubs = n()) %>%
arrange(desc(pubs))
#> # A tibble: 49 x 2
#> issued pubs
#> <dbl> <int>
#> 1 NA 12
#> 2 2003 5
#> 3 2006 4
#> 4 2012 4
#> 5 2014 4
#> 6 1985 3
#> 7 1993 3
#> 8 1994 3
#> 9 1998 3
#> 10 2007 3
#> # ... with 39 more rowsand of what type they are
random_dois %>%
group_by(type) %>%
summarize(pubs = n()) %>%
arrange(desc(pubs))
#> # A tibble: 7 x 2
#> type pubs
#> <chr> <int>
#> 1 journal-article 72
#> 2 book-chapter 8
#> 3 component 7
#> 4 proceedings-article 7
#> 5 dataset 3
#> 6 reference-entry 2
#> 7 journal-issue 1Now let's call oaDOI.org
oa_df <- roadoi::oadoi_fetch(dois = random_dois$DOI, email = "[email protected]")and merge the resulting information about open access full-text links with parts of our Crossref metadata-set
my_df <- random_dois %>%
select(DOI, type) %>%
left_join(oa_df, by = c("DOI" = "doi"))After gathering the data, reporting with R is very straightforward. You can even generate dynamic reports using R Markdown and related packages, thus making your study reproducible and transparent for others.
To display how many full-text links were found and which sources were used in a nicely formatted markdown-table using the knitr-package:
my_df %>%
group_by(is_oa) %>%
summarise(Articles = n()) %>%
mutate(Proportion = Articles / sum(Articles)) %>%
arrange(desc(Articles)) %>%
knitr::kable()| is_oa | Articles | Proportion |
|---|---|---|
| FALSE | 80 | 0.8 |
| TRUE | 20 | 0.2 |
How did oaDOI find those Open Access full-texts, which were characterized as best matches, and how are these OA types distributed over publication types?
my_df %>%
filter(is_oa == TRUE) %>%
tidyr::unnest(best_oa_location) %>%
group_by(evidence, type) %>%
summarise(Articles = n()) %>%
arrange(desc(Articles)) %>%
knitr::kable()| evidence | type | Articles |
|---|---|---|
| hybrid (via free pdf) | journal-article | 8 |
| oa journal (via publisher name) | component | 5 |
| oa repository (via BASE) | journal-article | 3 |
| hybrid (via crossref license) | journal-article | 2 |
| oa journal (via publisher name) | journal-article | 1 |
| oa repository (via BASE) | proceedings-article | 1 |
For more examples, see Piwowar et al. 2017.1 Together with the article, they shared their analysis of oaDOI-data as R Markdown supplement.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
License: MIT
Please use the issue tracker for bug reporting and feature requests.
Footnotes
-
Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., … Haustein, S. (2017). The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles (Version 1). PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3119v1 ↩ ↩2
